Transformers for Image Inpainting.
Transformers [1]} achieved good performance on many vision tasks by learning long-range interactions on sequential image patches.
Transformer is also employed to inpaint image in [2]}, [3]} at low resolution, and further guides the GAN-based CNN to produce high-quality results.
Unfortunately, directly using transformers to learn large image patches demands huge memory footprint and computations. In contrast, this work utilizes the transformer to build the LR holistic structure reconstruction, which helps to guide the HR image inpainting. Note that the upsampling for grayscale structures enjoys less ambiguities and information loss.
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